{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stochastic-backpropagation-and-approximate","title":"Stochastic Backpropagation and Approximate Inference in Deep Generative Models","arxiv_id":"1401.4082","date":"2014-01-16","proceeding":null,"authors":["Danilo Jimenez Rezende","Shakir Mohamed","Daan Wierstra"],"abstract":"We marry ideas from deep neural networks and approximate Bayesian inference\nto derive a generalised class of deep, directed generative models, endowed with\na new algorithm for scalable inference and learning. Our algorithm introduces a\nrecognition model to represent approximate posterior distributions, and that\nacts as a stochastic encoder of the data. We develop stochastic\nback-propagation -- rules for back-propagation through stochastic variables --\nand use this to develop an algorithm that allows for joint optimisation of the\nparameters of both the generative and recognition model. We demonstrate on\nseveral real-world data sets that the model generates realistic samples,\nprovides accurate imputations of missing data and is a useful tool for\nhigh-dimensional data visualisation.","url_abs":"http://arxiv.org/abs/1401.4082v3","url_pdf":"http://arxiv.org/pdf/1401.4082v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stochastic-backpropagation-and-approximate","repo_url":"https://github.com/ProcessMonitoringStellenboschUniversity/IFAC-VAE-Imputation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"stochastic-backpropagation-and-approximate","repo_url":"https://github.com/bccp/DeepUQ","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"stochastic-backpropagation-and-approximate","repo_url":"https://github.com/clinicalml/structuredinference","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"stochastic-backpropagation-and-approximate","repo_url":"https://github.com/geoffroeder/iwae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"stochastic-backpropagation-and-approximate","repo_url":"https://github.com/yburda/iwae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"stochastic-backpropagation-and-approximate","repo_url":"https://github.com/MindSpore-scientific-2/code-9/tree/main/iwae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1401.4082","atlas_url":"https://app.syntology.ai/?focus=1401.4082","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}